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Research Article

Thinking processes in code.org: A relational analysis approach to computational thinking

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Pages 545-566 | Received 31 Dec 2021, Accepted 06 Nov 2022, Published online: 13 Nov 2022

References

  • Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). A K-6 computational thinking curriculum framework: Implications for teacher knowledge. Journal of Educational Technology & Society, 19(3), 47–57. http://www.jstor.org/stable/jeductechsoci.19.3.47
  • Arslan, E., & Isbulan, O. (2021). The effect of individual and group learning on block-based programming self-efficacy and robotic programming attitudes of secondary school students. Malaysian Online Journal of Educational Technology, 9(1), 108–121. http://dx.doi.org/10.17220/mojet.2021.9.1.249
  • Baek, Y., Wang, S., Yang, D., Ching, Y.-H., Swanson, S., & Chittoori, B. (2019). revisiting second graders’ robotics with an understand/use-modify-create (U2MC) strategy. European Journal of STEM Education, 4(1), 07. https://doi.org/10.20897/ejsteme/5772
  • Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Learning & Leading With Technology, 38(6), 20–23. http://files.eric.ed.gov/fulltext/EJ918910.pdf
  • Basu, S., Biswas, G., Sengupta, P., Dickes, A., Clark, D., & Kinnebrew, J. (2016). Identifying middle school students’ challenges in computational thinking-based science learning. Research & Practice in Technology Enhanced Learning, 11(1), 1–35. https://doi.org/10.1186/s41039-016-0036-2
  • Bauer, M. (2000). Classical content analysis: A review. In M. Bauer & G. Gaskell (Eds.), Qualitative researching with text, image and sound (pp. 131–151). Sage.
  • Cakiroglu, U., & Mumcu, S. (2020). Focus-fight-finalize (3F): Problem-solving steps extracted from behavioral patterns in block based programming. Journal of Educational Computing Research. https://doi.org/10.1177/0735633120930673
  • Cetin, I., & Dubinsky, E. (2017). Reflective abstraction in computational thinking. The Journal of Mathematical Behavior, 47, 70–80. https://doi.org/10.1016/j.jmathb.2017.06.004
  • Città, G., Gentile, M., Allegra, M., Arrigo, M., Conti, D., Ottaviano, S., Reale, F., & Sciortino, M. (2019). The effects of mental rotation on computational thinking. Computers & Education, 141, 103613. https://doi.org/10.1016/j.compedu.2019.103613
  • Code.org. (2021). https://code.org/about
  • Cook, C., Goodman, N. D., & Schulz, L. E. (2011). Where science starts: Spontaneous experiments in preschoolers exploratory play. Cognition, 120(3), 341–349. https://doi.org/10.1016/j.cognition.2011.03.003
  • Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695.
  • Del Olmo-Muno, J., Cozar-Gutierrez, R., & Gonzalez-Calero, J. A. (2020). Computational thinking through unplugged activities in early years of primary education. Computers & Education, 150, 103832. https://doi.org/10.1016/j.compedu.2020.103832
  • Dietz, G., Landay, J. A., & Gweon, H. (2019). Building blocks of computational thinking: Young children’s developing capacities for problem decomposition. Proceedings of the 41st annual meeting of the cognitive science society (1647–1653).
  • Fiore, S. M., Graesser, A., Greiff, S., Griffin, P., Gong, B., Kyllonen, P., Massey, C., O’Neil, H., Pellgrino, J., Rothman, R., Soulé, H., & von Davier, A. (2017). Collaborative problem solving: Considerations for the national assessment of educational progress. Alexandria, VA: National Center for Education Statistics. https://nces.ed.gov/nationsreportcard/pdf/researchcenter/collaborative_problem_solving.pdf
  • Google Inc. & Gallup Inc. (2016). Trends in the state of computer science in U.S. K-12 Schools. http://goo.gl/j291E0
  • Greiff, S., Wüu, S., WuS, S., Csapó, B., Demetriou, A., Hautamäki, J., Graesser, A. C., & Martin, R. (2014). Domain-general problem solving skills and education in the 21st century. Educational Research Review, 13, 74–83. https://doi.org/10.1016/j.edurev.2014.10.002
  • Grover, S., & Pea, R. (2013). Computational thinking in K–12: A Review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051
  • Gunbatar, M. S., & Turan, B. (2019). The effect of block-based programming on the computational thinking skills of middle school students. Turkish Online Journal of Educational Technology, 2, 335–339. http://files.eric.ed.gov/fulltext/ED603404.pdf
  • Herold, B. (2017, April). Computer science for all in San Francisco schools: 7 early takeaways [Blog post]. EducationWeek. http://blogs.edweek.org/edweek/DigitalEducation/2017/04/computer_science_for_all_san_francisco_7_takeaways.html?cmp=SOC-SHR-twitter
  • Herring, S. C. (2004). Content analysis for new media: Rethinking the paradigm. New Research for New Media: Innovative Research Methodologies Symposium Working Papers and Readings, 2(12), 47–66.
  • Hsu, T. C., Chang, S. C., & Hung, Y. T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296–310. https://doi.org/10.1016/j.compedu.2018.07.004
  • Hu, Y., Chen, C. H., & Su, C. Y. (2021). Exploring the effectiveness and moderators of block-based visual programming on student learning: A meta-analysis. Journal of Educational Computing Research, 58(8), 1467–1493. https://doi.org/10.1177/0735633120945935
  • Jiang, B., Zhao, W., Zhang, N., & Qiu, F. (2022). Programming trajectories analytics in block-based programming language learning. Interactive Learning Environments, 30(1), 113–126. https://doi.org/10.1080/10494820.2019.1643741
  • Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48(4), 63–85. https://doi.org/10.1007/bf02300500
  • Jonassen, D. H. (2004). Learning to solve problems: An instructional design guide. Pfeiffer.
  • Jonassen, D. H. (2012). Problem typology. In Encyclopedia of the sciences of learning S. N.M Ed. Springer 2683–2686. https://doi.org/10.1007/978-1-4419-1428-6_209
  • Jonassen, D. H., & Hung, W. (2012). Problem solving. In Encyclopedia of the Sciences of Learning S. N.M Ed. Springer 2680–2683. https://doi.org/10.1007/978-1-4419-1428-6_208
  • Juškevičienė, A., & DagienĖ, V. (2018). Computational thinking relationship with digital competence. Informatics in Education, 17(2), 265–284. https://doi.org/10.15388/infedu.2018.14
  • K–12 computer science framework (2016). http://www.k12cs.org
  • Kafai, Y. B. (2016). From computational thinking to computational participation in K-12 education. Communications of the ACM, 59(8), 26–27. https://doi.org/10.1145/2955114
  • Kale, U., Akcaoglu, M., Cullen, T., Goh, D., Devine, L., Calvert, N., & Grise, K. (2018) Computational what? Relating computational thinking to teaching. TechTrends, 62(6) 574–58. https://doi.org/10.1007/s11528-018-0290-9
  • Kong, S. C., Chiu, M. M., & Lai, M. (2018). A study of primary school students’ interest, collaboration attitude, and programming empowerment in computational thinking education. Computers & Education, 127, 178–189. https://doi.org/10.1016/j.compedu.2018.08.026
  • Kramer, J. (2007). Is abstraction the key to computing? Communications of the ACM, 50(4), 36–42. https://doi.org/10.1145/1232743.1232745
  • Krippendorff, K. (1980). Validity in content analysis. In E. Mochmann, (Ed.) Computerstrategien fur die kommunikationsanalyse (pp. 69–112). Campus. http://repository.upenn.edu/asc_papers/291
  • Kyza, E. A., Georgiou, Y., Agesilaou, A., & Souropetsis, M. (2022). A cross-sectional study investigating primary school children’s coding practices and computational thinking using ScratchJr. Journal of Educational Computing Research, 60(1), 220–257. https://doi.org/10.1177/07356331211027387
  • Legare, C. H. (2012). Exploring explanation: Explaining inconsistent evidence informs exploratory, hypothesis‐testing behavior in young children. Child Development, 83(1), 173–185. https://doi.org/10.1111/j.1467-8624.2011.01691.x
  • Lesnick, J., Goerge, R., Smithgall, C., & Gwynne, J. (2010). Reading on grade level in third grade: How is it related to high school performance and college enrollment;Chapin Hall at the University of Chicago: https://www.chapinhall.org/wp-content/uploads/Reading_on_Grade_Level_111710.pdf
  • Lincoln, Y., & Guba, E. (1985). Naturalistic inquiry. Sage Publications.
  • Madda, J. M. (2016, March 9). White house and barack obama scale up TechHire initiative, push for more makerspaces in K-12. EdSurge. EdSurge. https://www.edsurge.com/news/2016-03-08-white-house-and-barack-obama-scaleuptechhireinitiative-push-for-more-makerspaces-in-k-12
  • Mayer, R. E., & Wittrock, M. C. (2006). Problem solving. In P. A. Alexander, P. H. Winne, P. A. Alexander, & P. H. Winne (Eds.), Handbook of educational psychology (pp. 287–303). Erlbaum.
  • Namli, A. N., & Aybek, B. (2022). An investigation of the effect of block-based programming and unplugged coding activities on fifth graders’ computational thinking skills, self-efficacy and academic performance. Contemporary Educational Technology, 14(1), 341. https://doi.org/10.30935/cedtech/11477
  • National Research Council. (2012). Education for life and work: Developing transferable knowledge and skills in the 21st century. The National Academies Press. https://doi.org/10.17226/13398
  • Neuendorf, K. A. (2002). Defining content analysis. In K. A. Neuendorf (Ed.), Content analysis guidebook (pp. 1–31). Thousand Oaks, CA: Sage.
  • Newman, M. E. J. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical Review E, 74(3), 036104. https://doi.org/10.1103/PhysRevE.74.036104
  • OECD. (2003). PISA 2003 assessment framework: Mathematics, reading, science and problem solving knowledge and skills. http://www.oecd.org/edu/school/programmeforinternationalstudentassessmentpisa/33694881.pdf
  • OECD. (2015). OECD skills outlook 2015: Youth, skills and employability. http://www.oecd.org/edu/oecd-skills-outlook-2015-9789264234178-en.htm
  • Ozgur, H. (2020). Relationships between computational thinking skills, ways of thinking and demographic variables: A structural equation modeling. International Journal of Research in Education and Science (IJRES), 6(2), 299–314. https://doi.org/10.46328/ijres.v6i2.862
  • Palts, T., & Pedaste, M. (2020). A model for developing computational thinking skills. Informatics in Education, 19(1), 113–128. https://doi.org/10.15388/infedu.2020.06
  • Papert, S., & Harel, I. (1991). Situating constructionism. Constructionism, 36(2), 1–11. http://hcs64.com/teaching%20CS/papert-situating_constructionism.pdf
  • Pedersen, T. L. (2018). ggraph: An implementation of grammar of graphics for graphs and networks. Pedersen. Retrieved July 27, 2019, fromhttps://CRAN.R-project.org/package=ggraph
  • Pedersen, T. L. (2019). tidygraph: A tidy API for graph manipulation. Pedersen. Retrieved July 27, 2019, from https://CRAN.R-project.org/package=tidygraph
  • Polya, G. (1957). How to solve it. Doubleday/Anchor.
  • Pretz, J. E., Naples, A. J., & Sternberg, R. J. (2003). Recognizing, defining, and representing problems. In J. E. Davidson & R. J. Sternburg (Eds.), The psychology of problem solving (pp. 3–30). Cambridge university press.
  • Qiu, Y. (2021). (2021). showtext: Using fonts more easily in R graphs. R package version 0.9-4. Qiu. https://CRAN.R-project.org/package=showtext
  • R Core Team. (2014). R: A language and environment for statistical computing. R foundation for statistical computing. Retrieved June 2, 2019, from https://www.R-project.org
  • Rich, P. J., Mason, S. L., & O’Leary, J. (2021). Measuring the effect of continuous professional development on elementary teachers’ self-efficacy to teach coding and computational thinking. Computers & Education, 168, 104196. https://doi.org/10.1016/j.compedu.2021.104196
  • Rich, K. M., Strickland, C., Binkowski, T. A., Moran, C., & Franklin, D. (2018). K–8 learning trajectories derived from research literature: Sequence, repetition, conditionals. ACM Inroads, 9(1), 46–55. https://doi.org/10.1145/3183508
  • Rijke, W. J., Bollen, L., Eysink, T. H., & Tolboom, J. L. (2018). Computational thinking in primary school: An examination of abstraction and decomposition in different age groups. Informatics in Education, 17(1), 77–92. https://doi.org/10.15388/infedu.2018.05
  • RStudio Team. (2021). RStudio: Integrated Development Environment for R. http://www.rstudio.com/
  • Sáez-López, J. M., Román-González, M., & Vázquez-Cano, E. (2016). Visual programming languages integrated across the curriculum in elementary school: A two year case study using “Scratch” in five schools. Computers & Education, 97, 129–141. https://doi.org/10.1016/j.compedu.2016.03.003
  • Satuluri, V., & Parthasarathy, S. (2011, March). Symmetrizations for clustering directed graphs. In Proceedings of the 14th international conference on extending database technology (pp. 343–354).
  • Sawyer, R. K., & Goldman, K. J. (2010). Collaborative learning of computer science concepts. In K. Littleton & C. Howe (Eds.), Educational dialogues: Understanding and promoting productive interaction (pp. 323–345). Routledge.
  • Schoch, D. (2019). graphlayouts: Additional layout algorithms for network visualizations Schoch. Retrieved July 27, 2019, from https://CRAN.R-project.org/package=graphlayouts
  • Scott, J., & Carrington, P. J. (2014). The SAGE handbook of social network analysis. SAGE Publications Ltd. https://doi.org/10.4135/9781446294413
  • Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158. https://doi.org/10.1016/j.edurev.2017.09.003
  • Smith, M. (2016). WhiteHouse.Gov blog: Computer science for all [Web log post]. WhiteHouse.Gov blog. https://www.whitehouse.gov
  • Sonnleitner, P., Brunner, M., Keller, U., & Martin, R. (2014). Differential relations between facets of complex problem solving and students’ immigration background. Journal of Educational Psychology, 106(3), 681–695. https://doi.org/10.1037/a0035506
  • Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education, 148, 103798. https://doi.org/10.1016/j.compedu.2019.103798
  • Tikva, C., & Tambouris, E. (2021). Mapping computational thinking through programming in K-12 education: A conceptual model based on a systematic literature Review. Computers & Education, 162, 104083. https://doi.org/10.1016/j.compedu.2020.104083
  • Tsai, M. J., Liang, J. C., Lee, S. W. Y., & Hsu, C. Y. (2022). Structural validation for the developmental model of computational thinking. Journal of Educational Computing Research, 60(1), 56–73. https://doi.org/10.1177/07356331211017794
  • van Merriënboer, J. J. G. (2013). Perspectives on problem solving and instruction. Computers & Education, 64, 153–160. https://doi.org/10.1016/j.compedu.2012.11.025
  • Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking in compulsory education: Towards an agenda for research and practice. Education and Information Technologies, 20(4), 715–728. http://doi.org/10.1007/s10639-015-9412-6
  • Voskoglou, M. G., & Buckley, S. (2012). Problem solving and computational thinking in a learning environment. Egyptian Computer Science Journal, 36(4), 28–46. https://doi.org/10.48550/arXiv.1212.0750
  • Wang, X. C., Choi, Y., Benson, K., Eggleston, C., & Weber, D. (2021). Teacher’s role in fostering preschoolers’ computational thinking: An exploratory case study. Early Education and Development, 32(1), 26–48. https://doi.org/10.1080/10409289.2020.1759012
  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Press Syndicate of the University of Cambridge.
  • Wei, X., Lin, L., Meng, N., Tan, W., & Kong, S. C. (2020). The effectiveness of partial pair programming on elementary school students’ computational thinking skills and self-efficacy. Computers & Education, 160, 1–15. https://doi.org/10.1016/j.compedu.2020.104023
  • The White House. (2017). A commitment to the American workforce: Expanding access to high quality STEM and computer science education provides more pathways to good jobs. https://www.whitehouse.gov/the-press-office/2017/09/25/expanding-access-high-quality-stem-and-computer-science-education
  • Wickham, H. (2017). tidyverse: Easily install and load the ’Tidyverse’. Wickham. Retrieved June 2, 2019, from https://CRAN.R-project.org/package=tidyverse
  • Wing, J. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A, 366, 3717–3725. https://doi.org/10.1098/rsta.2008.01181881
  • Wing, J. M. (2014). Computational thinking benefits society. 40th Anniversary Blog of Social Issues in Computing, 2014, 26. http://socialissues.cs.toronto.edu/index.html%3Fp=279.html
  • Zhang, L., & Nouri, J. (2019). A systematic review of learning computational thinking through scratch in K-9. Computers & Education, 141, 103607. https://doi.org/10.1016/j.compedu.2019.103607
  • Zhan, Z., He, W., Yi, X., & Ma, S. (2022). Effect of unplugged programming teaching aids on children’s computational thinking and classroom interaction: With respect to Piaget’s four stages theory. Journal of Educational Computing Research, 07356331211057143. https://doi.org/10.1177/07356331211057143

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